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DTSTAMP:20240214T070241Z
LOCATION:Darling Harbour Theatre\, Level 2 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231212T093000
DTEND;TZID=Australia/Melbourne:20231212T124500
UID:siggraphasia_SIGGRAPH Asia 2023_sess209_papers_155@linklings.com
SUMMARY:Drivable Avatar Clothing: Faithful Full-Body Telepresence with Dyn
 amic Clothing Driven by Sparse RGB-D Input
DESCRIPTION:Technical Papers\n\nDonglai Xiang (Carnegie Mellon University/
 Robotics Institute, Meta Reality Labs Research); Fabian Prada, Zhe Cao, Ka
 iwen Guo, and Chenglei Wu (Meta Reality Labs Research); Jessica Hodgins (C
 arnegie Mellon University); and Timur Bagautdinov (Meta Reality Labs Resea
 rch)\n\nClothing is an important part of human appearance but challenging 
 to model in photorealistic avatars. In this work we present avatars with d
 ynamically moving loose clothing that can be faithfully driven by sparse R
 GB-D inputs as well as body and face motion. We propose a Neural Iterative
  Closest Point (N-ICP) algorithm that can efficiently track the coarse gar
 ment shape given sparse depth input. Given the coarse tracking results, th
 e input RGB-D images are then remapped to texel-aligned features, which ar
 e fed into the drivable avatar models to faithfully reconstruct appearance
  details. We evaluate our method against recent image-driven synthesis bas
 elines, and conduct a comprehensive analysis of the N-ICP algorithm. We de
 monstrate that our method can generalize to a novel testing environment, w
 hile preserving the ability to produce high-fidelity and faithful clothing
  dynamics and appearance.\n\nRegistration Category: Full Access, Enhanced 
 Access, Trade Exhibitor, Experience Hall Exhibitor
URL:https://asia.siggraph.org/2023/full-program?id=papers_155&sess=sess209
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